Unsupervised deep anomaly detection for multi-sensor time-series signals
Nowadays, multi-sensor technologies are applied in many fields, eg, Health Care (HC),
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …
Fault detection in wireless sensor networks through the random forest classifier
Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in
unpredictable and hazardous environments. This makes WSN prone to failures such as …
unpredictable and hazardous environments. This makes WSN prone to failures such as …
RANet: Network intrusion detection with group-gating convolutional neural network
X Zhang, F Yang, Y Hu, Z Tian, W Liu, Y Li… - Journal of Network and …, 2022 - Elsevier
With the rapid increase of human activities in cyberspace, various network intrusions are
tended to be frequent and hidden. Network intrusion detection (NID) has attracted more and …
tended to be frequent and hidden. Network intrusion detection (NID) has attracted more and …
Unknown security attack detection using shallow and deep ANN classifiers
Advancements in machine learning and artificial intelligence have been widely utilised in
the security domain, including but not limited to intrusion detection techniques. With the …
the security domain, including but not limited to intrusion detection techniques. With the …
Analysis of fault classifiers to detect the faults and node failures in a wireless sensor network
Technology evaluation in the electronics field leads to the great development of Wireless
Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in …
Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in …
Autoencoder-based unsupervised intrusion detection using multi-scale convolutional recurrent networks
The massive growth of network traffic data leads to a large volume of datasets. Labeling
these datasets for identifying intrusion attacks is very laborious and error-prone …
these datasets for identifying intrusion attacks is very laborious and error-prone …
Data-driven fault detection process using correlation based clustering
YJ Yoo - Computers in Industry, 2020 - Elsevier
This paper presents an algorithm for the fault detection process using correlation based
clustering. Conventional clustering-based fault detection calculates the fault index through …
clustering. Conventional clustering-based fault detection calculates the fault index through …
Detection and quantification of anomalies in communication networks based on LSTM-ARIMA combined model
S Xue, H Chen, X Zheng - International Journal of Machine Learning and …, 2022 - Springer
The anomaly detection for communication networks is significant for improve the quality of
communication services and network reliability. However, traditional communication …
communication services and network reliability. However, traditional communication …
Data curation and quality evaluation for machine learning-based cyber intrusion detection
Intrusion detection is an essential task for protecting the cyber environment from attacks.
Many studies have proposed sophisticated models to detect intrusions from a large amount …
Many studies have proposed sophisticated models to detect intrusions from a large amount …
An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networks
The authors introduce an unsupervised Intrusion Detection System designed to detect zero‐
day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This …
day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This …